Demand response (DR) is a mechanism to manage customer's consumption of electricity in response to supply conditions, for example, having electricity customers reduce their consumption at critical times or in response to market prices. Demand response is generally used to encourage consumers to reduce demand, thereby reducing the peak demand for electricity. Demand response gives the consumers the ability to voluntarily trim or reduce their electricity usage at specific times of the day during high electricity prices, or during emergencies.
In other words, demand response is a resource that allows end-use electricity customers to reduce their electricity usage in a given time period, or shift that usage to another time period, in response to a price signal, a financial incentive, an environmental condition, or a reliability signal. Demand response saves ratepayer's money by lowering peak time energy usage that is high-priced. This lowers the price of wholesale energy, and in turn, retail rates. Demand response may also prevent rolling blackouts by offsetting the need for more electricity generation and can mitigate generator market power.
There are two general classes of DR program—dispatchable and non-dispatchable. Non-dispatchable DR is not dependent on explicit signaling from the load serving entity (LSE) to the participant. Examples of non-dispatchable DR include time-of-use (TOU) and real time pricing (RTP) programs. In contrast, dispatchable demand response entails the load serving entity sending explicit signals to participants to reduce energy consumption during a specified time window. The signals can either communicate economic incentives to participants or control participant devices directly. An example of the former is critical peak price (CPP) demand response programs, while the latter are referred to as direct load control (DLC) demand response.
For both types of dispatchable demand response there are switches and signal receptors at the participant site that must function properly for the desired load curtailment to be realized. An important issue facing load serving entities is that as large numbers of these devices fail, the overall effectiveness of the demand response (DR) program is severely degraded. Load serving entities can perform physical on-site inspections to monitor and fix broken devices, but this approach is extremely expensive to scale up to many thousands of participants if the inspections are not well-targeted to those sites with a high probability of device failure. Alternatively, waiting for participants to self-report a failed device before visiting a site can result in consistently poor demand response performance over the course of the program.
Given the above, there is a huge value in a system that can analyze real-time and historical data on demand response event performance and automatically identify those participants that have a high-probability of device failure. The demand response optimization and management system for real time system contains an operability analysis engine (OAE) that employs advanced, customized statistical methods to generate a probability that a given participant has a device failure based on historical interval meter data.